00. kel_1 davis,bagozzi and warshaw_1989_user acceptance of computer technology _a comparison of two...

MANAGEMENT SCIENCE Vol 35 No 8 August 1989

Pfrr~iedrn C S A

USER ACCEPTANCE OF COMPUTER TECHNOLOGY A COMPARISON OF TWO THEORETICAL MODELS

FRED D DAVIS RICHARD P BAGOZZI A N D PAUL R WARSHAW

School ofBusiness Administration LTni~~crsitj~of Michigan Ann Arbor Michigan 4 8 109- 1234 School of Bzisiness Adininistration LTnivcrsityof Michigan Ann Arbor Michigan 4 8 109- 1234 School of B~llsiness Ad~ninistratioiz Calvornia Polytechnic State Uni~~crsitj) San Lzlis Obisl~a

Calforniu 93407

Computer systems cannot improve organizational performance if they arent used Unfortu- nately resistance to end-user systems by managers and professionals is a widespread problem To better predict explain and increase user acceptance we need to better understand why people accept or reject computers This research addresses the ability to piedict peoples computer acceptance from a measure of their intentions and the ability to explain their intentions in terms of their attitudes subjective norms perceived usefulness perceived ease of use and related variables In a longitudinal study of 107 users intentions to use a specific system measured after a one- hour introduction to the system were correlated 035 with system use 14 weeks later The intention- usage correlation was 063 at the end of this time period Perceived usefulness strongly influenced peoples intentions explaining more than half of the variance in intentions at the end of 14 weeks Perceived ease of use had a small but significant effect on intentions as well although this effect subsided over time Attitudes only partially mediated the effects of these beliefs on intentions Subjective norms had no effect on intentions These results suggest the possibility of simple but powerful models of the determinants of user acceptance with practical value for evaluating systems and guiding managerial interventions aimed at reducing the problem of underutilized computer technology (INFORMATION TECHNOLOGY USER ACCEPTANCE INTENTION MODELS)

1 Introduction Organizational investments in computer-based tools to support planning decision-

making and communication processes are inherently risky Unlike clerical papenvork- processing systems these end-user computing tools often require managers and professionals to interact directly with hardware and software However end-users are often unwilling to use available computer systems that if used would generate significant performance gains (eg Alavi and Henderson 198 1 Nickerson 198 1 Swanson 1988) The raw power of computer technology continues to improve tenfold each decade (Peled 1987) making sophisticated applications economically feasible As technical barriers disappear a pivotal factor in harnessing this expanding power becomes our ability to create applications that people are willing to use Identifying the appropriate functional and interface characteristics to be included in end-user systems has proven more challenging and subtle than expected (March 1987 Mitroff and Mason 1983) Recognizing the difficulty of specifying the right system requirements based on their own logic and intuition designers are seeking methods for evaluating the acceptability of systems as early as possible in the design and implementation process (eg Alavi 1984 Bewley et al 1983 Branscomb and Thomas 1984 Gould and Lewis 1985) Practitioners and researchers require a better understanding of why people resist using computers in order to devise practical methods for evaluating systems predicting how users will respond to them and improving user acceptance by altering the nature of systems and the processes by which they are implemented

Understanding why people accept or reject computers has proven to be one of the most challenging issues in information systems (IS) research (Swanson 1988) Investi- gators have studied the impact of users internal beliefs and attitudes on their usage

Accepted by Richard M Burton received November 10 1987 This paper has been with the authors 4 months for 2 revisions

Copynght O 1989 The lnst~tute of Management Sciences

983 USER ACCEPTANCE OF COMPUTER TECHNOLOGY

behavior (DeSanctis 1983 Fuerst and Cheney 1982 Ginzberg 198 1 Ives Olson and Baroudi 1983 Lucas 1975 Robey 1979 Schultz and Slevin 1975 Srinivasan 1985 Swanson 1974 1987) and how these internal beliefs and attitudes are in turn influenced by various external factors including the systems technical design characteristics (Ben- basat and Dexter 1986 Benbasat Dexter and Todd 1986 Dickson DeSanctis and McBride 1986 Gould Conti and Hovanyecz 1983 Malone 1981 ) user involvement in system development (Baroudi Olson and Ives 1986 Franz and Robey 1986) the type of system development process used (eg Alavi 1984 King and Rodriguez 198 1 ) the nature of the implementation process (Ginzberg 1978 Vertinsky Barth and Mitchell 1975 Zand and Sorensen 1975) and cognitive style (Huber 1983) In general however these research findings have been mixed and inconclusive In part this may be due to the wide array of different belief attitude and satisfaction measures which have been employed often without adequate theoretical or psychometric justification Research progress may be stimulated by the establishment of an integrating paradigm to guide theory development and to provide a common frame of reference within which to integrate various research streams

Information systems (IS) investigators have suggested intention models from social psychology as a potential theoretical foundation for research on the determinants of user behavior (Swanson 1982 Christie 198 1 ) Fishbein and Ajzens ( 1975) (Ajzen and Fish- bein 1980) theory of reasoned action (TRA) is an especially well-researched intention model that has proven successful in predicting and explaining behavior across a wide variety of domains TRA is very general designed to explain virtually any human behavior (Ajzen and Fishbein 1980 p 4) and should therefore be appropriate for studying the determinants of computer usage behavior as a special case

Davis ( 1986) introduced an adaptation of TRA the technology acceptance model (TAM) which is specifically meant to explain computer usage behavior TAM uses TRA as a theoretical basis for specifying the causal linkages between two key beliefs perceived usefulness and perceived ease of use and users attitudes intentions and actual computer adoption behavior TAM is considerably less general than TRA designed to apply only to computer usage behavior but because it incorporates findings accumulated from over a decade of IS research it may be especially well-suited for modeling computer acceptance

In the present research we empirically examine the ability of TRA and TAM to predict and explain user acceptance and rejection of computer-based technology We are particularly interested in how well we can predict and explain future user behavior from simple measures taken after a very brief period ofinteraction with a system This scenario characterizes the type of evaluations made in practice after pre-purchase trial usage or interaction with a prototype system under development (eg Alavi 1984) After presenting the major characteristics of the two models we discuss a longitudinal study of 107 MBA students which provides empirical data for assessing how well the models predict and explain voluntary usage of a word processing system We then address the prospects for synthesizing elements of the two models in order to arrive at a more complete view of the determinants of user acceptance

2 Theory of Reasoned Action (TRA)

TRA is a widely studied model from social psychology which is concerned with the determinants of consciously intended behaviors (Ajzen and Fishbein 1980 Fishbein and Ajzen 1975) According to TRA a persons performance of a specified behavior is determined by his or her behavioral intention (BI) to perform the behavior and BI is jointly determined by the persons attitude (A) and subjective norm (SN) concerning the behavior in question (Figure 1 ) with relative weights typically estimated by regression

BI = A + SN ( 1 )

984 FRED D DAVIS RICHARD P BAGOZZI AND PAUL R WARSHAW

Evaluations Behavior (A)

ActualIntention Behavior

Normative Beliefs Subjective and Motivation to Norm

comply (Z nbmci) (SN)

FIGURE 1 Theory of Reasoned Action (TRA)

BI is a measure of the strength of ones intention to perform a specified behavior (eg Fishbein and Ajzen 1975 p 288) A is defined as an individuals positive or negative feelings (evaluative affect) about performing the target behavior (eg Fishbein and Ajzen 1975 p 216) Subjective norm refers to the persons perception that most people who are important to him think he should or should not perform the behavior in question (Fishbein and Ajzen 1975 p 302)

According to TRA a persons attitude toward a behavior is determined by his or her salient belhfs ( b )about consequences of performing the behavior multiplied by the evaluation (e) of those consequences

Beliefs (b) are defined as the individuals subjective probability that performing the target behavior will result in consequence i The evaluation term (e) refers to an implicit evaluative response to the consequence (Fishbein and Ajzen 1975 p 29) Equation ( 2 ) represents an information-processing view of attitude formation and change which posits that external stimuli influence attitudes only indirectly through changes in the persons belief structure (Ajzen and Fishbein 1980 pp 82-86)

TRA theorizes that an individuals subjective norm (SN) is determined by a multi- plicative function of his or her normative beliefs ( n b ) ie perceived expectations of specific referent individuals or groups and his or her motivation to comply (me) with these expectations (Fishbein and Ajzen 1975 p 302)

TRA is a general model and as such it does not specify the beliefs that are operative for a particular behavior Researchers using TRA must first identify the beliefs that are salient for subjects regarding the behavior under investigation Fishbein and Ajzen ( 1975 p 218) and Ajzen and Fishbein ( 1980 p 68) suggest eliciting five to nine salient beliefs using free response interviews with representative members of the subject population They recommend using modal salient beliefs for the population obtained by taking the beliefs most frequently elicited from a representative sample of the population

A particularly helpful aspect of TRA from an IS perspective is its assertion that any other factors that influence behavior do so only indirectly by influencing A SN or their relative weights Thus variables such as system design characteristics user characteristics (including cognitive style and other personality variables) task characteristics nature of the development or implementation process political influences organizational structure and so on would fall into this category which Fishbein and Ajzen (Ajzen and Fishbein 1975) refer to as external variables This implies that TRA mediates the impact of uncontrollable environmental variables and controllable interventions on user behavior If so then TRA captures the internal psychological variables through which numerous external variables studied in IS research achieve their influence on user acceptance and

985 USER ACCEPTANCE O F COMPUTER TECHNOLOGY

may provide a common frame of reference within which to integrate various disparate lines of inquiry

A substantial body of empirical data in support of TRA has accumulated (Ajzen and Fishbein 1980 Fishbein and Ajzen 1975 Ryan and Bonfield 1975 Sheppard Hartwick and Warshaw in press) TRA has been widely used in applied research settings spanning a variety of subject areas while at the same time stimulating a great deal of theoretical research aimed at understanding the theorys limitations testing key assumptions and analyzing various refinements and extensions (Bagozzi 198 1 1982 1984 Saltzer 198 1 Warshaw 1980a b Warshaw and Davis 1984 1985 1986 Warshaw Sheppard and Hastwick in press)

3 Technology Acceptance Model (TAM)

TAM introduced by Davis ( 1986) is an adaptation of TRA specifically tailored for modeling user acceptance of information systems The goal of TAM is to provide an explanation of the determinants of computer acceptance that is general capable of explaining user behavior across a broad range of end-user computing technologies and user populations while at the same time being both parsimonious and theoretically justified Ideally one would like a model that is helpful not only for prediction but also for explanation so that researchers and practitioners can identify why a particular system may be unacceptable and pursue appropriate corrective steps A key purpose of TAM therefore is to provide a basis for tracing the impact of external factors on internal beliefs attitudes and intentions TAM was formulated in an attempt to achieve these goals by identifying a small number of fundamental variables suggested by previous research dealing with the cognitive and affective determinants of computer acceptance and using TRA as a theoretical backdrop for modeling the theoretical relationships among these variables Several adaptations to the basic TRA approach were made supported by available theory and evidence based on these goals for TAM

TAM posits that two particular beliefs percellled zisefillness and percellled easr of use are of primary relevance for computer acceptance behaviors (Figure 2 ) Perceived usefulness ( U ) is defined as the prospective users subjective probability that using a specific application system will increase his or her job performance within an organizational context Perceived ease of use (EOU) refers to the degree to which the prospective user expects the target system to be free of effort As discussed further below several studies have found variables similar to these to be linked to attitudes and usage In addition factor analyses suggest that U and EOU are statistically distinct dimensions (Hauser and Shugan 1980 Larcker and Lessig 1980 Swanson 1987)

Similar to TRA TAM postulates that computer usage is determined by BI but differs in that BI is viewed as being jointly determined by the persons attitude toward using the system (A) and perceived usefulness ( U ) with relative weights estimated by regression

BI = A + U ( 4 )

-Perceived Usefulness

(U) -- -Attitude Behavioral Actual External Toward Intention to System Variables Using (A) Use (BI) Use

Perceived Ease of Use

FIGURE2 Technology Acceptance Model (TAM)


The A-BI relationship represented in TAM implies that all else being equal people form intentions to perform behaviors toward which they have positive affect The A-BI relationship is fundamental to TRA and to related models presented by Triandis ( 1977) and Bagozzi ( 1981 ) Although the direct effect of a belief (such as U ) on BI runs counter to TRA alternative intention models provide theoretical justification and empirical evidence of direct belief-intention links (Bagozzi 1982 Triandis 1977 Brinberg 1979) The U-BI relationship in equation ( 4 ) is based on the idea that within organizational settings people form intentions toward behaviors they believe will increase their job performance over and above whatever positive or negative feelings may be evoked toward the behavior per se This is because enhanced performance is instrumental to achieving various rewards that are extrinsic to the content of the work itself such as pay increases and promotions (eg Vroom 1964) Intentions toward such means-end behaviors are theorized to be based largely on cognitive decision rules to improve performance without each time requiring a reappraisal of how improved performance contributes to purposes and goals higher in ones goal hierarchy and therefore without necessarily activating the positive affect associated with performance-contingent rewards (Bagozzi 1982 Vallacher and Wegner 1985) If affect is not fully activated when deciding whether to use a particular system ones attitude would not be expected to completely capture the impact of performance considerations on ones intention Hence the U-BI relationship in TAM represents the resulting direct effect hypothesizing that people form intentions toward using computer systems based largely on a cognitive appraisal of how it will improve their performance

TAM does not include TRAs subjective norm (SN) as a determinant of BI As Fishbein and Ajzen acknowledge ( 1975 p 304) this is one of least understood aspects of TRA It is difficult to disentangle direct effects of SN on BI from indirect effects via A SN may influence BI indirectly via A due to internalization and identification processes or influence BI directly via compliance (Kelman 1958 Warshaw 1980b) Although it is generally thought that computer use by managers and professionals is mostly voluntary ( DeSanctis 1983 Robey 1979 Swanson 1987 ) in some cases people may use a system in order to comply with mandates from their superiors rather than due to their own feelings and beliefs about using it However as Warshaw ( 1980b) points out standard measures of SN do not appear to differentiate compliance from internalization and identification Complicating matters further A may influence SN for example due to the false consensus effect in which people project their own attitudes to others (eg Oliver and Bearden 1985) Because of its uncertain theoretical and psychometric status SN was not included in TAM However since we measured SN in our study in order to examine TRA we can test whether SN explains any of BIs variance beyond that accounted for by A and U

Previous IS research contains empirical evidence in favor of the A-BI and U-BI relationships represented in equation (4 ) Although BI per se has seldom been measured in IS research several studies have measured A using a variety of measurement method- ologies and have observed a significant link between A and usage (for review see Swanson 1982) Usefulness and variables similar to it such as perceptions of performance impacts relevance and importance have also been linked to usage (DeSanctis 1983 Robey 1979 Schultz and Slevin 1975 Swanson 1987) Although the measures employed in these studies were quite varied and often unvalidated the similarity of the findings obtained from differing contexts suggests the possibility of fairly robust underlying relationships

According to TAM A is jointly determined by U and EOU with relative weights statistically estimated by linear regression

A = U + EOU ( 5 )


This equation is inspired by TRAs view that attitudes toward a behavior are determined by relevant beliefs As discussed above TAM posits that U has a direct effect on BI over and above A Equation ( 5 ) indicates that U influences A as well Although we contend that ones affect toward a behavior need not fully incorporate affect toward any rewards due to performance outcomes contingent on that behavior we acknowledge that through learning and affective-cognitive consistency mechanisms (Bagozzi 1982) positively valued outcomes often increase ones affect toward the means to achieving those outcomes (Peak 1955 Rosenberg 1956 Vroom 1964) Hence U is hypothesized to have a positive influence on A (as shown in equation (5 ) above) Previous IS research contains empirical evidence consistent with a U-A link (Barrett Thornton and Cabe 1968 Schultz and Slevin 1975 )

EOU is also hypothesized to have a significant effect on A TAM distinguishes two basic mechanisms by which EOU influences attitudes and behavior self-efficacy and instrumentality The easier a system is to interact with the greater should be the users sense of efficacy (Bandura 1982) and personal control (Lepper 1985 ) regarding his or her ability to carry out the sequences of behavior needed to operate the system Efficacy is thought to operate autonomously from instrumental determinants of behavior (Bandura 1982) and influences affect effort persistence and motivation due to inborn drives for competence and self-determination (Bandura 1982 Deci 1975 ) Efficacy is one of the major factors theorized to underly intrinsic motivation (Bandura 1982 Lepper 1985) The direct EOU-A relationship is meant to capture this intrinsically motivating aspect of EOU (Carroll and Thomas 1988 Davis 1986 Malone 198 1 )

Improvements in EOU may also be instrumental contributing to increased performance Effort saved due to improved EOU may be redeployed enabling a person to accomplish more work for the same effort To the extent that increased EOU contributes to improved performance as would be expected EOU would have a direct effect on U

U = EOU + External Variables

Hence we view U and EOU as distinct but related constructs As indicated earlier empirical evidence from factor analyses suggests these are distinct dimensions At the same time empirical associations between variables similar to U and EOU have been observed in prior research (Barrett Thornton and Cabe 1968 Swanson 1987)

As equation ( 6 ) implies perceived usefulness ( U ) can be affected by various external variables over and above EOU For example consider two forecasting systems which are equally easy to operate If one of them produces an objectively more accurate forecast it would likely be seen as the more useful ( U ) system despite the EOU parity Likewise if one graphics program produces higher quality graphs than its equally easy-to-use counterparts it should be consideredmore useful Hence the objective design characteristics of a system can have a direct effect on U in addition to indirect effects via EOU Several investigators have found a significant relationship between system characteristics and measures similar to perceived usefulness (eg Benbasat and Dexter 1986 Benbasat Dexter and Todd 1986 Miller 1977 ) Similarly educational programs designed to pur- suade potential users of the power offered by a given system and the degree to which it may improve users productivity could well influence U Learning based on feedback is another type of external variable apt to influence usefulness beliefs

Perceived ease of use ( E ) is also theorized to be determined by external variables

EOU = External Variables ( 7 )

Many system features such as menus icons mice and touch screens are specifically intended to enhance usability (Bewley et al 1983) The impact of system features on EOU has been documented (eg Benbasat Dexter and Todd 1986 Bewley et al 1983


Dickson DeSanctis and McBride 1986 Miller 1977) Training documentation and user support consultants are other external factors which may also influence EOU

Despite their similarity TAM and TRA differ in several theoretical aspects some of which warrant explanation Both TAM and TRA posit that A is determined by ones relevant beliefs Two key differences between how TAM and TRA model the determinants of A should be pointed out First using TRA salient beliefs are elicited anew for each new context The resulting beliefs are considered idiosyncratic to the specific context not to be generalized for example to other systems and users (Ajzen and Fishbein 1980) In contrast TAMS U and EOU are postulated a priori and are meant to be fairly general determinants of user acceptance This approach was chosen in an attempt to arrive at a belief set that more readily generalizes to different computer systems and user populations Second whereas TRA sums together all beliefs (6) multiplied by corresponding evaluation weights (el) into a single construct (equation ( 2 ) above) TAM treats U and EOU as two fundamental and distinct constructs Modeling beliefs in this disaggregated manner enables one to compare the relative influence of each belief in determining A providing important diagnostic information Further representing beliefs separately allows the researcher to better trace the influence of external variables such as system features user characteristics and the like on ultimate behavior From a practical standpoint this enables an investigator to better formulate strategies for influencing user acceptance via controllable external interventions that have measurable influences on particular beliefs For example some strategies may focus on increasing EOU such as providing an improved user interface or better training Other strategies may target U by increasing the accuracy or amount of information accessible through a system

Following the view that U and EOU are distinct constructs their relative influences on A are statistically estimated using linear regression (or related methods such as conjoint measurement or structural equations) Within TAM U and EOU are not multiplied by self-stated evaluation weights Given that neither beliefs nor evaluations are ratio-scaled the estimated relationship (correlation or regression weight) between A and the product of a belief and evaluation is ambiguous since it would be sensitive to allowable but theoretically irrelevant linear scale transformations of either the belief or evaluation (for further explanation cf Bagozzi 1984 Ryan and Bonfield 1975 Schmidt 1973) On the other hand as Fishbein and Ajzen ( 1975 p 238) point out omitting the evaluation terms may be inisleading in cases where some people in a sample hold positive evaluations while others hold negative evaluations of the same outcome However we expect U and EOU to be positively valued outcomes for most people When the evaluative polarity of an outcome is fairly homogeneous across subjects the corresponding belief tends to be monotonically related to attitudes and statistically estimated weights tend to accurately capture the actual usage of information cues (Einhorn Kleinmuntz and Kleinmuntz 1979 Hogarth 1974) and generally predict dependent variables at least as well as subjective weights (Bass and Wilkie 1973 Stahl and Grigsby 1987 Shoemaker and Waid 1982) A similar rationale underlies equation ( 1 ) of TRA where the relative influences of A and SN on BI are statistically estimated as opposed to self-stated One caveat is that to the extent that individuals within a sample differ substantially with respect to the motivating impact of U and EOU our statistically estimated weights may become distorted In view of the tradeoffs involved we chose to use statistically-estimated weights within TAM to gauge the comparative influence of U and EOU on A

External variables represented in equations ( 6 ) and ( 7 ) provide the bridge between the internal beliefs attitudes and intentions represented in TAM and the various individual differences situational constraints and managerially controllable interventions impinging on behavior TRA similarly hypothesizes that external variables influence behavior only indirectly via A SN or their relative weights Although our primary interest in the par-


ticular study described below is to examine our ability to predict and explain user behavior with TAM working from U and EOU forward to user acceptance we explicitly include external variables in our description of the model to underscore the fact that one of its purposes is to provide a foundation for studying the impact of external variables on user behavior Our goal in the study reported below is to examine the relationships among EOU U A BI and system usage in order to see how well we can predict and explain user acceptance with TAM In so doing we hope to gain insight about TAMs strengths and weaknesses by comparing it to the well-established TRA

4 Research Questions

Our analysis of TRA and TAM raises several research questions which the study described below was designed to address

( 1) How well do intentions predict usage Both models predict behavior from behavioral intention (BI) Of particular interest is the ability to predict future usage based on a brief (eg one-hour) hands-on introduction to a system This would mirror the applied situations in which these models may have particular value If after briefly exposing potential users to a candidate system that is being considered for purchase and organizational implementation management is able to take measurements that predict the future level of adoption a golno-go decision on the specific system could be made from a more informed standpoint Similarly as new systems are being developed early prototypes can be tested and intention ratings used to assess the prospects of the design before a final system is built

( 2 ) How well do TRA and TAM explain intentions to use a system We hypothesize that TRA and TAM will both explain a significant proportion of the variance in peoples behavioral intention to use a specific system Although prediction in and of itself is of value to system designers and implementors explaining whj people choose to use or not use a system is also of great value Therefore we are also interested in the relative impact on BI of TRAs A SN and C hieiconstructs and TAMs U and EOU

( 3 ) Do attitudes mediate the effect of beliefs on intentions A key principle of TRA is that attitudes fully mediate the effects of beliefs on intentions Yet as discussed above direct belief-intention relationships have been observed before One of the theoretical virtues of the attitude construct is that it purports to capture the influence of beliefs Much of its value is foregone if it only partially mediates the impact of beliefs

( 4 ) Is there some alternative theoretical formulation that better accounts for observed data We recognize that any model is an abstraction of reality and is likely to have its own particular strengths and weaknesses Our goal is less that of proving or disproving TRA or TAM than in using them to investigate user behavior We are therefore interested in exploring alternative specifications perhaps bringing together the best of both models in our pursuit of a theoretical account of user acceptance

5 Empirical Study

In order to assess TRA and TAM we gathered data from 107 full-time MBA students during their first of four semesters in the MBA program at the University of Michigan A word processing program Writeone was available for use by these students in two public computer laboratories located at the Michigan Business School Word processing was selected as a test application because ( 1 ) it is a voluntarily used package unlike spreadsheets and statistical programs that students are required to use for one or more courses ( 2 ) students would face opportunities to use a word processor throughout the MBA program for memos letters reports resumes and the like and (3 )word processors


are among the most frequently used categories of software among practicing managers (Benson 1983 Honan 1986 Lee 1986)

At the beginning of the semester MBA students are given a one-hour introduction to the WriteOne software as part of a computer orientation At the end of this introduction we administered the first wave of a questionnaire containing measures of the TRA and TAM variables A second questionnaire administered at the end of the semester 14 weeks later contained measures of the TAM and TRA variables as well as a 2-item measure of self-reported usage

Salient Belief Elicitation

To determine the modal salient beliefs for usage of the WriteOne software telephone interviews were conducted with 40 MBA students who were about to enter their second year of the MBA program We chose to elicit beliefs from second-year students since they are very similar to the entering first-year students in terms of background and abilities and had just completed a year of study during which their introduction and access to the WriteOne system was identical to that which entering first-year students would face Since we wanted to have the questionnaire prepared in advance of the first 1-hour exposure the first-year students would have with WriteOne so we could track changes in their beliefs over time it would not have been practical to ask first-year students their beliefs prior to this initial indoctrination Although they are likely to have had similar basic concerns as the second-year students first-year students were not expected to be in a position to articulate those concerns as well with regard to the WriteOne system specifically since they would be unlikely to ever1 know that such a system existed We would have faced greater risk of omitting beliefs which would have become salient by the time first-year students completed their initial usage and learning and usage of WriteOne On the other hand using second year students increased the risk of including some beliefs that are nonsalient for first year students after their initial one-hour introduction However the consequences of omitting a salient belief are considered more severe than those of including a nonsalient one To omit a salient belief ie one that does significantly influence attitude degrades the validity of the TRA belief summation term (by omitting a source of systematic variance) whereas including a nonsalient belief ie one that does not influence attitude degrades the reliability of the belief summation term (by adding a source of random variance) Moreover beliefs lower in the salience hierarchy contribute less to ones total attitude than do more salient ones (Fishbein and Ajzen 1975 p 223) In view of the tradeoffs involved we elected to pursue a more inclusive belief set by eliciting it from second-year students

Interviewees were asked to list separately the advantages disadvantages and anything else they associate with becoming a user of WriteOne (This procedure is recommended by Ajzen and Fishbein 1980 p 68) Beliefs referring to nearly identical outcomes using alternative wording were classified as the same item and the most common wording was utilized The seven most frequently mentioned outcomes were chosen This belief set complied with the criteria for modal beliefs since each belief was mentioned by more than 20 of the sample and the set contained more than 75 of the beliefs emitted The seven resulting belief items in order of frequency of mention are

1 Id save time in creating and editing documents 2 Id find it easier to create and edit documents 3 My documents would be of a better quality 4 I would not use alternative word processing packages 5 Id experience problems gaining access to the computing center due to crowdedness 6 Id become dependent on WriteOne 7 I would not use WriteOne after I leave the MBA program

USER ACCEPTANCE O F COMPUTER TECHNOLOGY 99 1

Qziesf ionnaire

Both TRA and TAM are being used to explain a specific behavior (usage) toward a specific target (WriteOne) within a specific contest (the MBA program) The time period of usage although not explicitly indicated is implicitly bounded by the context of the MBA program The definition and measurement of model constructs correspond in specificity to these characteristics of the behavioral criterion so that the measures of intentions attitudes and beliefs are worded in reference to the specific target action and context elements but are relatively nonspecific with respect to time frame (for further discussion of the correspondence issue see Ajzen and Fishbein 1980) BI A SN b and e were all operationalized according to Ajzen and Fishbeins ( 1980 Appendix A) recommended guidelines

TAMS U and EOU are each operationalized with 4-item instruments resulting from an extensive measure development and validation procedure As described in Davis ( 1986) the measure development process consisted of generating 14 candidate items for each construct based on their definitions pre-testing the items to refine their wording and to pare the item sets down to 10 items per construct and assessing the reliability (using Cronbach alpha) and validity (using the multitrait-multimethod approach) of the 10-item scales High levels of convergent and discriminant validity of the 10-item scales were observed and Cronbach alpha reliabilities were 097 for U and 09 1 for EOU Item analyses were used to streamline the scales to 6 items per construct and new data again revealed high validity and reliability (alpha of 097 for U and 093 for EOU) Further item analyses were performed to arrive at the 4-item scales used in the present research The four ease of use items were Learning to operate WriteOne would be easy for me I would find it easy to get WriteOne to do what I want it to do It would be easy for me to become skillful at using WriteOne and I would find WriteOne easy to use The four usefulness items were Using WriteOne would improve my performance in the MBA program Using WriteOne in the MBA program would increase my productivity Using WriteOne would enhance my effectiveness in the MBA program and I would find WriteOne useful in the MBA program The usefulness and ease of use items were measured with 7-point scales having likely-unlikely endpoints and the anchor points extremely quite slightly and neither (identical to the format used for operationalizing TRA beliefs and recommended by Ajzen and Fishbein 1980 Ap- pendix A )

System usage is measured using 2 questions regarding the frequency with which the respondent currently uses WriteOne The first was a 7-point scale with the adjectives frequent and infrequent at the endpoints The second was a check the box format with categories for current use of not at all less than once a week about once a week 2 or 3 times a week 4 to 6 times a week about once a day more than once a day These are typical of the kinds of self-reported measures often used to operationalize system usage particularly in cases where objective usage metrics are not available Objective usage logs were not practical in the present context since the word processing software was located on personal computers and subjects use different computers as well as different applications from one session to the next Self-reported frequency measures should not be regarded as precise measures of actual usage frequency although previous research suggests they are appropriate as relative measures (Blair and Burton 1987 Hartley et al 1977)

Results

Scale Reliabilities The two-item BI scale obtained a Cronbach alpha reliability of 084 at time 1 (beginning of the semester) and 090 at time 2 (end of the semester) The

992 FRED D DAVIS RlCHARD P BAGOZZl AND PAUL R WARSHAW

four-item A scale obtained reliabilities of 085 and 082 at times 1 and 2 respectively The four-item U scale achieved a reliability of 095 and 092 for the two points in time and the four-item EOU scale obtained reliability coefficients of 09 1 and 090 for time 1 and time 2 SN the bs and the es were each operationalized with single-item scales per TRA and hence no internal consistency assessments of reliability are possible The two-item usage scale administered in the second questionnaire achieved an alpha of 079 These scale reliabilities are all at levels considered adequate for behavioral research

Expaining Usage As expected BI was significantly correlated with usage Intentions measured right after the Writeone introduction were correlated 035 with usage frequency 14 weeks later (Table 1 ) Intentions and usage measured contemporaneously at the end of the semester correlated 063 Also consistent with the theories none of the other TRA or TAM variables (A SN C be U or E ) had a significant efect on usage over and

TABLE l

Predicr ing 011d Es1laiiiir7g ampage Inieniions N I I ~Attit lldcs usit11 ille Tilcoq of Rc~usoned Aciio17 (TR-I ) und rile Teciinolog~~ Accq~luiice Vlod(l ( T A M )

Time I immediately After Time 2

1 Hr lntro 14 Weeks Later

Equation R 2 Beta R 2 Beta

( I ) Explaining Usage at Time 2 From B1 Measured at Times 1 and 2 (Common to both Models)

Usage (Time 2) = BI 012 040 BI 035 063

(2) TRA BI = A + SN

A SN

(3) TAM B l = A + U

A U

A = U + EOU U

EOU

U = EOU EOU

Note p lt 005 p lt 001 p lt 0001 B1 = Behavioral Intention A = Attitude SN = Subjective Norm U = Perceived Usefulness C be = Sum of Beliefs Times Evaluations EOU = Perceived Ease of Use


above intentions at either time 1 or time 2 which suggests that intentions fully mediated the effects of these other variables on usage

Explaining Behavlol-a1 Intention (BI) As theorized TRA and TAM both explained a significant proportion of the variance in BI (Table 1 ) TRA accounted for 32 of the variance at time 1 and 26 of the variance at time 2 TAM explained 47 and 5 1 of BIs variance at times 1 and 2 respectively Looking at the individual determinants of BI within TRA A had a strong significant influence on BI (P = 055 time 1 P = 048 time 2) whereas SN had no significant effect in either time period ( b = 007 and 010 respectively) Within TAM U has a very strong effect in both time periods ( P = 048 and 06 1 respectively ) while A had a smaller effect in time 1 (0 = 027) and a nonsignificant effect in time 2 (P = 016) The increased influence of U from time 1 to time 2 is noteworthy Equation ( l b ) Table 2 shows that U adds significant explanatory power beyond A and SN at both time 1 and time 2 underscoring the influential role of U

In both models unexpected direct belief-intention relationships were observed Counter to TRA the belief summation term C behad a significant direct effect on BI over and above A and SN in time period 2 ( P = 02 1 ) but not in time period 1 ( P = 008) (Table 2 ) Counter to TAM EOU had a significant direct effect on BI over and above A and U in time period 1 ( 0 = 020) but not time period 2 (P = 01 1) (Table 2 ) Hence attitude appears to mediate the effects of beliefs on intentions even less than postulated by TRA and TAM

TABLE 2

Hierarchical Regres~ion T e ~ t s Jbr Relutioi~~hipsEtsleccd ro bc Ironsigniiciitil

T ~ m e1 Time 2


(1) Behavioral Intention (BI) (a) BI = A + SN + C be

A SN

C hip

(b) BI = A + U + SN A

u SN

(2) Attitude (A) A = U + E + C b e 038 044

U 058 035 E 001 018

C hie 010 032h

p lt 005 p lt 001 p lt 0001 a Expected and found nonsignificant b Expected nonsignificant but found significant


Explaining Attitude As expected both TAM and TRA explain a significant percentage of variance in attitude (Table 1 ) TRA explained 7 of As variance at time 1 and 30 at time 2 TAM explained 37 and 36 at times 1 and 2 respectively U has a strong significant effect on A in both time periods ( P = 061 and 050 respectively) although EOU is significant at time 2 only ( P = 024)

In both models there were some interesting developmental changes over time in the relationship among beliefs A and BI Within TAM at time 1 EOU appears to have a direct effect on BI ( P = 020) with no indirect effect through A or U at time 2 EOUs effect is entirely indirect via U and the A-BI link becomes nonsignificant TRAs belief summation term C be has a significant effect on A above and beyond U and EOU in time period 2 ( p = 032) but not in time period 1 (P = 010) (Table 2 ) Our analysis below investigates the nature of these patterns further by analyzing the internal structure of TRAs beliefs and analyzing their relationship to U and EOU A and BI

Further Analysis of Belief Structure In order to gain greater insight into the nature of TRAs beliefs as well as their relationship to U and EOU a factor analysis was conducted Table 3 shows a varimax rotated principal components factor analysis of TRAs 7 belief items and TAMs 4 U items and 4 EOU items using a 10 eigenvalue cutoff criterion For time period 1 a five-factor solution was obtained with the 7 TRA beliefs factoring into three distinct dimensions the other two factors corresponding to TAMs U and EOU TRA beliefs 1 2 and 3 load on a common factor which taps specific aspects of expected performance gains Whereas TAMs U is a comparatively general assessment of expected performance gains (eg increase my productivity) TRAs first three items are more specific aspects (ie saving time in creating and editing documents finding it easier to create and edit documents and making higher quality documents) We will refer to this specific usefulness construct comprised of TRAs first three belief items as Us Consistent with this interpretation Us correlates significantly with U ( r = 046 p lt 0001 for time 1 and r = 065 p lt 0001 for time 2 ) At time period 2 a four-factor solution was obtained with Us converging to TAMs U to form a single factor We will denote this combined 7-item usefulness index U for total usefulness Cronbach alpha reliabilities for U were 085 and 093 for time 1 and 2 respectively

In both time periods TRA beliefs 4 and 6 loaded on a common factor which has to do with becoming dependent on WriteOne (would become dependent would not use alternatives ) which we will denote D TRA items 5 and 7 loaded on a common factor at time 1 and are concerned with access to WriteOne both while in the MBA program (item 5) and after leaving the program (item 7) We will denote this factor ACC At time 2 only item 5 loaded on this factor with item 7 showing a tendency to load on U instead (loading = -045)

Hence the factor analysis of TRA and TAM beliefs suggests the existence of belief dimensions concerning usefulness ease of use dependency and accessibility Overall perceived usefulness (U) appeared to have separate specific (Us) and general ( U ) dimensions at time 1 which converged to form a common dimension at time 2 Perceived accessibility (ACC) was comprised of 2 items (TRA beliefs 5 and 7) at time 1 and only 1 item (belief 5 ) at time 2

Hybrid Intention hfodels The factor analysis above provided some interesting insights into the dimensional structure of the beliefs underlying user acceptance Combining the beliefs of TRA and TAM into a single analysis may yield a better perspective on the determinants of BI than that provided by either model by itself Given that A was generally not found to intervene between beliefs and intentions our approach in this section is to first assess the impact on intentions of the beliefs identified in the factor analysis above and then test whether A mediates these belief-intention relationships We estimated the effect on BI of the five beliefs identified by the factor analysis U Us EOU D and ACC


TABLE 3

Factor Ana1ysis of TAM and TR4 Belief Iterns

Belief Item 1 2

Time I Factors

3 4 5 1

Time 2 Factors

2 3 4

(a) TRA Items

TRA 1 TRA2 TRA3 TRA4 TRA5 TRA6 TRA7

028 027 018 017 002 008

-026

005 013 003

-011 009

-009 000

089 088 080 009 006 017

-012

(b) TAM Usefulness (U) Items

(c) TAM Ease of Use (EOU) Items

EOU 1 -008 084 008 EOU2 00 1 090 003 EOU3 -005 091 003 EOU4 010 091 007

Eigen 483 335 151 Var 323 223 101 Cum 323 546 647

(see Table 4 ) Together these variables explained 5 1 of BIs variance in time 1 and 61 in time 2 U Us and EOU were significant for time 1 but EOU became nonsignificant in time 2 In addition Us increased in importance from time 1 ( b = 020) to time 2 (= 039) Next we combined the two usefulness subdimensions to form the U index and ran another regression U was highly significant in both time periods ( = 059 and 071 respectively ) and EOU was significant for time period 1 only ( = 020)

In order to test whether A fully mediated either the EOU-BI or U-BI relationships we introduced A into the second equation This had little effect on the coeflicients for either U or EOU suggesting that although A may partially mediate these relationships it did not fully mediate them The relationship between EOU and U hypothesized by TAM was nonsignificant for time 1 but became significant for time 2 (= 024) Therefore the causal structure suggested is that U had a direct impact on BI in both time periods and EOU had a direct effect on BI at time 1 and an indirect effect via U at time 2

In order to obtain more precise estimates of these significant effects regressions omitting nonsignificant variables were run (see Final Models Table 4 ) At time 1 U and EOU accounted for 45 of the variance in intention with coefficients of 062 and 020 respectively At time 2 U by itself accounted for 57 of BIs variance ( = 076) and EOU had a small but significant effect on U ( = 024)

As mentioned earlier to the extent that people are heterogeneous in their evaluation of or motivation toward performance our statistical estimate of the usefulness-intention link may be distorted In order to test for whether differences in motivation moderated


TABLE 4

Hjlbrid Intention Models

Time 1 Time 2


BI = U + Us + EOU + D + ACC U

U EOU

D ACC

05 1

-

BI = U + EOU + D + ACC Ul

EOU D

ACC

050

-

U = EOU 002

Final Models

A Time I BI = U + EOU

Ul EOU

B Time 2 BI = U U = EOU

p lt 005 p lt 001 p lt 0001 Note U = TAMS general perceived usefulness scale (4 items) U = TRAs specific

usefulness scale (items 1-3) U = Total usefulness index (comprised of U and U 7 items)

the usefulness-intention relationship we asked subjects to report the extent to which they believed performance in the MBA program is important to getting a good job By hierarchical regression this question did not significantly interact with U in either time period We also used the sum of the three evaluation terms (e)corresponding to TRA belief items 1-3 as an indicant of subjects evaluation of usefulness as an outcome This also did not significantly interact with usefulness in either time period Thus in our sample it appears that individuals did not differ enough in either ( 1) their perceived impact of performance in the MBA program on their getting a good job or ( 2 ) their evaluation of performance to seriously distort our estimate of the effect of U on BI

The picture that emerges is that U is a strong determinant of BI in both time periods and that EOU also has a significant effect on BI at time 1 but not at time 2 EOUs direct effect on BI in time period 1 developed into a significant indirect effect through usefulness in time period 2

6 Conclusions

Our results yield three main insights concerning the determinants of managerial computer use


( 1) Peoples computer use can be predicted reasonably well from their intentions (2 ) Perceived usefulness is a major determinant of peoples intentions to use computers ( 3 ) Perceived ease of use is a significant secondary determinant of peoples intentions

to use computers Although our data provided mixed support for the two specific theoretical models that

guided our investigation TRA and TAM their confluence led to the identification of a more parsimonious causal structure that is powerful for predicting and explaining user behavior based on only three theoretical constructs behavioral intention ( BI ) perceived usefulness ( U ) and perceived ease of use (EOU) Specifically after the one-hour introduction to the system peoples intentions were jointly determined by perceived usefulness ( p = 062) and perceived ease of use ( P = 020) At the end of 14 weeks intention was directly affected by usefulness alone (P = 079) with ease of use affecting intention only indirectly via usefulness (P = 024) This simple model accounted for 45 and 57 of the variance in intentions at the beginning and end of the 14-week study period respectively

Both TRA and TAM postulated that BI is the major determinant of usage behavior that behavior should be predictable from measures of BI and that any other factors that influence user behavior do so indirectly by influencing BI These hypotheses were all supported by our data Intentions measured after a one-hour introduction to a word processing system were correlated 035 with behavior 14 weeks later This is promising for those who wish to evaluate systems very early in their development and cannot obtain extensive user experience with prototypes in order to assess its potential acceptability This is also promising for those who would like to assess user reactions to systems used on a trial basis in advance of purchase decisions Intentions and usage measured contemporaneously correlated 063 Given that intentions are subject to change between the time of intention measurement and behavioral performance one would expect the intention-behavior correlation to diminish with increased elapsed time (Ajzen and Fish- bein 1975 p 370) In addition at time 1 given the limited experience with the system peoples intentions would not be expected to be extremely well-formed and stable Con- sistent with expectations hierarchical regression tests indicated that none of the other variables studied influenced behavior directly over and above intention

In order to place these intention-behavior correlations in perspective we can compare them to ( a ) past experience using intention measures outside the IS domain and ( b ) correlations between usage and various predictors reported in the IS literature In a meta- analysis of non-IS studies Sheppard Hartwick and Warshaw (in press) calculated a frequency-weighted average intention-behavior correlation of 038 based on 15 14 subjects for goal-type behaviors The intention-usage correlations of 035 and 063 obtained in the present study compare favorably with this meta-analysis Although the intention- usage relationship per se has been essentially overlooked in the IS literature usage predictions based on numerous other variables have been investigated Ginzberg ( 1981) obtained a correlation of 022 between a measure of users realism of expectations and usage DeSanctis ( 1983) obtained correlations around 025 between motivational force and DSS usage Swanson ( 1987) obtained a 020 correlation between usage and a variable referred to as value which is similar to perceived usefulness Robey obtained a striking 079 between usage and Schultz and Slevins ( 1975) performance factor which is also similar to perceived usefulness Baroudi Olson and Ives ( 1986) found both user information satisfaction and user involvement to be correlated 028 with system usage Sri- nivasan ( 1985) found relationships varying from -023 to 038 between various measures of user satisfaction and usage Overall the predictive correlations obtained in IS research have varied widely from -023 up to the 079 correlation obtained by Robey (1979) with typical values falling in the 020-030 range The 035 and 063 correlations obtained


for the two time periods investigated in the present research compare favorably with these previous IS findings

Both TRA and TAM hypothesized that expected performance impacts due to using the specified system ie perceived usefulness would be a major determinant of BI Interestingly the models arrived at this hypothesis by very different lines of reasoning Within TAM perceived usefulness was specified a priori based on the observation that variables having to do with performance gains had surfaced as influential determinants of user acceptance in previous IS studies In contrast TRA called for eliciting the specific perceived consequences held by specific subjects concerning the specific system under investigation Using this method the first three beliefs elicited were specific performance gains These three TRA beliefs which were much more specific than TAMs perceived usefulness measures (eg save time in creating and editing documents versus increase my productivity) loaded together on a single dimension in a factor analysis Although TRAs specific usefulness dimension (Us) was factorially distinct from TAMs U at time 1 (just after the one-hour demonstration) they were significantly correlated ( r = 046) Fourteen weeks later (time 2) the general and specific items converged to load on single fact01

But why was it the case that U had more influence on BI than Us right after the one- hour introduction whereas Us increased in influence and converged to U over time One possibility relates to the concreteness-abstractness distinction from psychology (eg Mervis and Rosch 198 1 ) As Bettman and Sujan ( 1987) point out novice consumers are more apt to process choice alternatives using abstract general criteria since they have not undergone the learning needed to understand and make judgments about more concrete specific criteria This learning process could account for the increased importance of Us over time as well as its convergence to U as the subjects in our study gained additional knowledge about the consequences of using of WriteOne over the 14-week period following the initial introduction The implication is that since people form general impressions of usefulness quickly after a brief period of using a system the more general usefulness construct provides a somewhat better explanation of intentions at such a point in time

Combining the 3 specific TRA usefulness beliefs and the 4 general TAM usefulness beliefs yielded a total index of usefulness U that had a major impact on BI in both time periods Indeed subjects appeared to form their intentions toward using the word processing system based principally on their expectations that it would improve their performance within the MBA program Among the other beliefs studied only EOU had a significant effect on BI and only at time 1 Over time as users learned to effectively operate the word processor the direct effect of ease of use on BI disappeared being supplanted by an indirect effect via U Following our theorizing early on people appeared to process EOU from a self-efficacy perspective appraising how likely they would be to succeed at learning to use the system given they tried As learning progressed over time this concern became less salient and EOU evolved into a more instrumental issue re- flecting considerations of how the relative effort of using the system would affect the overall performance impact the system offered (U )

The lack of a significant SN-BI effect was surprising given previous IS research stressing the importance oftop management support and user involvement There are two reasons to interpret this finding narrowly First as pointed out in our discussion of TAM compared to other measures recommended for TRA (Ajzen and Fishbein 1980) the SN scale is particularly weak from a psychometric standpoint More sophisticated methods for assessing the specific types of social influence processes at work in a computer acceptance context are clearly needed Second the specific application studied word processing is fairly personal and individual and may be driven less by social influences compared


to more multi-person applications such as electronic mail project management or group decision support systems Further research is needed to address the generalizability of our SN findings to better understand the nature of social influences and to investigate conditions and mechanisms governing the impact of social influences on usage behavior

The absence of a significant effect of accessibility on intentions or behavior was also surprising in light of the importance of this variable in studies of information source usage (Culnan 1983 OReilly 1982) Since our measure of accessibility was nonvalidated having been developed by exploratory factor analysis psychometric weaknesses may be partly at fault In addition although access was a salient concern frequently mentioned in the belief elicitation the system under investigation was fairly uniformly accessible to all respondents Accessibility may well have played a more predominant role if greater variations in system accessibility were present in the study Also surprising was the finding that attitudes intervened between beliefs and intentions far less than hypothesized by either TRA or TAM Although some work on the direct effect of beliefs has been done (eg Bagozzi 1982 Brinberg 1979 Triandis 1977) more research is needed to identify the conditions under which attitudes mediate the belief-intention link In either case the attitude construct did little to help elucidate the causal linkages between beliefs and intentions in the present study since at best it only partially mediated these relationships

There are several aspects of the present study which circumscribe the extent to which our findings generalize MBA students are not completely representative of the entire population of managers and professionals whose computer usage behavior we would like to model These students are younger and as a group probably more computer literate than their counterparts in industry Hence EOU may have been less an issue for this sample than it would have been for managers and professionals more generally The WriteOne system while typical of the types of systems available to end users is still only one system With more complex or difficult systems ease of use may have had a greater impact on intentions These subjects were also probably more highly motivated to perform well than the general population which may have caused perceived usefulness to take on greater importance than it generally would Further research on these variables and relationships in other settings will sharpen our understanding of their generalizability Additional theoretical constructs such as computer anxiety and instrinsic motivation may profitably be brought into the analysis There is reason for optimism however Extensive experience with intention measures in other contexts has consistently supported their role as predictors of an individuals behavior (eg Ajzen and Fishbein 1980) In addition the usefulness-intention relationship observed in the present data is so strong that it seems unlikely to be totally idiosyncratic If models similar to the final models presented in Table 4 do generalize to other contexts we will be moving to a situation in which powerful yet simple models for predicting and explaining user acceptance are available

7 Practical Implications

What do our results imply for managerial practice When planning a new system IS practitioners would like to be able to predict whether the new system will be acceptable to users diagnose the reasons why a planned system may not be fully acceptable to users and to take corrective action to increase the acceptability of the system in order to enhance the business impact resulting from the large investments in time and money associated with introducing new information technologies into organizations The present research is relevant to all of these concerns

As Ginzberg ( 1981 ) pointed out in his discussion of early-warning techniques for anticipating potential user acceptance problems at the initial design stages of a system development effort a relatively small fraction of a projects resources has been expended


and yet many of the design decisions concerning the functional and interface features of the new system are made Moreover at this early point in the process there is greatest flexibility in altering the proposed design since little if any actual programming or equip- ment procurement has occurred Hence this would appear to represent an ideal time to measure user assessments of a proposed system in order to get an early reading on its acceptability Standing in the way however has been the lack of good predictive models The present research contributes to the solution of this dilemma by helping to identify and provide valid measures of key variables linked to user behavior

A key challenge facing user acceptance testing early in the development process is the difficulty of conveying to users in a realistic way what a proposed system will consist o f The paper designs that typify the status of a system at the initial design stage may not be an adequate stimulus for users to form accurate assessments However several techniques can be used to overcome this shortcoming Rapid prototypers user interface management systems and videotape mockups are increasingly being used to create realistic facades of what a system will consist of at a fraction of the cost of building the complete system This raises the question whether a brief exposure (eg less than an hour) to a prototype system is adequate to permit the potential user to acquire stable well-formed beliefs Especially relevant here is our finding that after a one-hour hands-on introduction people formed general perceptions of a systems usefulness that were strongly linked to usage intentions and their intentions were significantly correlated with their future acceptance of the system Further research into the effectiveness of noninteractive mockups such as videotapes is important in order to establish how far upstream in the development process we can push user acceptance testing Throughout such evaluation programs practitioners and researchers should not lose sight of the fact that usage is only a necessary but not sufficient condition for realizing performance improvements due to information technology if a system is not really useful (even if users perceive it to be) it should not be marketed to users

Our findings have implications for improving user acceptance as well Many designers believe that the key barrier to user acceptance is the lack of user friendliness of current systems and that adding user interfaces that increase usability is the key to success (eg Branscomb and Thomas 1985 ) Yet our data indicates that although ease of use is clearly important the usefulness of the system is even more important and should not be overlooked Users may be willing to tolerate a difficult interface in order to access functionality that is very important while no amount of ease of use will be able to compensate for a system that doesnt do a useful task Diagnostic measurements of the kind were proposing should augment designers intuition and help them identify and evaluate strategies for enhancing user acceptance Future research is needed to test the generality of the observed usefulness-ease of use tradeoff and to assess the impact of external interventions on these internal behavioral determinants

Overall research in this direction should yield practical techniques to evaluate and improve the acceptability of end-user systems The ability to take robust well-formed measures of the determinants of user acceptance early in the development process is undoubtedly going to have an impact on our ability to weed out bad systems refine the rest and generally cut the risk of delivering finished systems that get rejected by users

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User Acceptance of Computer Technology A Comparison of Two Theoretical ModelsFred D Davis Richard P Bagozzi Paul R WarshawManagement Science Vol 35 No 8 (Aug 1989) pp 982-1003Stable URL

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behavior (DeSanctis 1983 Fuerst and Cheney 1982 Ginzberg 198 1 Ives Olson and Baroudi 1983 Lucas 1975 Robey 1979 Schultz and Slevin 1975 Srinivasan 1985 Swanson 1974 1987) and how these internal beliefs and attitudes are in turn influenced by various external factors including the systems technical design characteristics (Ben- basat and Dexter 1986 Benbasat Dexter and Todd 1986 Dickson DeSanctis and McBride 1986 Gould Conti and Hovanyecz 1983 Malone 1981 ) user involvement in system development (Baroudi Olson and Ives 1986 Franz and Robey 1986) the type of system development process used (eg Alavi 1984 King and Rodriguez 198 1 ) the nature of the implementation process (Ginzberg 1978 Vertinsky Barth and Mitchell 1975 Zand and Sorensen 1975) and cognitive style (Huber 1983) In general however these research findings have been mixed and inconclusive In part this may be due to the wide array of different belief attitude and satisfaction measures which have been employed often without adequate theoretical or psychometric justification Research progress may be stimulated by the establishment of an integrating paradigm to guide theory development and to provide a common frame of reference within which to integrate various research streams

Information systems (IS) investigators have suggested intention models from social psychology as a potential theoretical foundation for research on the determinants of user behavior (Swanson 1982 Christie 198 1 ) Fishbein and Ajzens ( 1975) (Ajzen and Fish- bein 1980) theory of reasoned action (TRA) is an especially well-researched intention model that has proven successful in predicting and explaining behavior across a wide variety of domains TRA is very general designed to explain virtually any human behavior (Ajzen and Fishbein 1980 p 4) and should therefore be appropriate for studying the determinants of computer usage behavior as a special case

Davis ( 1986) introduced an adaptation of TRA the technology acceptance model (TAM) which is specifically meant to explain computer usage behavior TAM uses TRA as a theoretical basis for specifying the causal linkages between two key beliefs perceived usefulness and perceived ease of use and users attitudes intentions and actual computer adoption behavior TAM is considerably less general than TRA designed to apply only to computer usage behavior but because it incorporates findings accumulated from over a decade of IS research it may be especially well-suited for modeling computer acceptance

In the present research we empirically examine the ability of TRA and TAM to predict and explain user acceptance and rejection of computer-based technology We are particularly interested in how well we can predict and explain future user behavior from simple measures taken after a very brief period ofinteraction with a system This scenario characterizes the type of evaluations made in practice after pre-purchase trial usage or interaction with a prototype system under development (eg Alavi 1984) After presenting the major characteristics of the two models we discuss a longitudinal study of 107 MBA students which provides empirical data for assessing how well the models predict and explain voluntary usage of a word processing system We then address the prospects for synthesizing elements of the two models in order to arrive at a more complete view of the determinants of user acceptance

2 Theory of Reasoned Action (TRA)

TRA is a widely studied model from social psychology which is concerned with the determinants of consciously intended behaviors (Ajzen and Fishbein 1980 Fishbein and Ajzen 1975) According to TRA a persons performance of a specified behavior is determined by his or her behavioral intention (BI) to perform the behavior and BI is jointly determined by the persons attitude (A) and subjective norm (SN) concerning the behavior in question (Figure 1 ) with relative weights typically estimated by regression

BI = A + SN ( 1 )




















BI = A + U ( 4 )










A = U + EOU ( 5 )
























5 Empirical Study










Qziesf ionnaire




Results





TABLE l






Usage (Time 2) = BI 012 040 BI 035 063

(2) TRA BI = A + SN

A SN

(3) TAM B l = A + U

A U

A = U + EOU U

EOU

U = EOU EOU






TABLE 2


T ~ m e1 Time 2



A SN

C hip

(b) BI = A + U + SN A

u SN


U 058 035 E 001 018

C hie 010 032h










TABLE 3


Belief Item 1 2

Time I Factors

3 4 5 1

Time 2 Factors

2 3 4

(a) TRA Items


028 027 018 017 002 008

-026

005 013 003

-011 009

-009 000

089 088 080 009 006 017

-012



EOU 1 -008 084 008 EOU2 00 1 090 003 EOU3 -005 091 003 EOU4 010 091 007

Eigen 483 335 151 Var 323 223 101 Cum 323 546 647






TABLE 4


Time 1 Time 2



U EOU

D ACC

05 1

-


EOU D

ACC

050

-

U = EOU 002

Final Models


Ul EOU






6 Conclusions





























Management Scl 27 ( 1981) 1309-1323










































































References







httpwwwjstororg












httpwwwjstororg

LINKED CITATIONS- of 2 -




















BI = A + U ( 4 )










A = U + EOU ( 5 )
























5 Empirical Study










Qziesf ionnaire




Results





TABLE l






Usage (Time 2) = BI 012 040 BI 035 063

(2) TRA BI = A + SN

A SN

(3) TAM B l = A + U

A U

A = U + EOU U

EOU

U = EOU EOU






TABLE 2


T ~ m e1 Time 2



A SN

C hip

(b) BI = A + U + SN A

u SN


U 058 035 E 001 018

C hie 010 032h










TABLE 3


Belief Item 1 2

Time I Factors

3 4 5 1

Time 2 Factors

2 3 4

(a) TRA Items


028 027 018 017 002 008

-026

005 013 003

-011 009

-009 000

089 088 080 009 006 017

-012



EOU 1 -008 084 008 EOU2 00 1 090 003 EOU3 -005 091 003 EOU4 010 091 007

Eigen 483 335 151 Var 323 223 101 Cum 323 546 647






TABLE 4


Time 1 Time 2



U EOU

D ACC

05 1

-


EOU D

ACC

050

-

U = EOU 002

Final Models


Ul EOU






6 Conclusions





























Management Scl 27 ( 1981) 1309-1323










































































References







httpwwwjstororg












httpwwwjstororg







A = U + EOU ( 5 )
























5 Empirical Study










Qziesf ionnaire




Results





TABLE l






Usage (Time 2) = BI 012 040 BI 035 063

(2) TRA BI = A + SN

A SN

(3) TAM B l = A + U

A U

A = U + EOU U

EOU

U = EOU EOU






TABLE 2


T ~ m e1 Time 2



A SN

C hip

(b) BI = A + U + SN A

u SN


U 058 035 E 001 018

C hie 010 032h










TABLE 3


Belief Item 1 2

Time I Factors

3 4 5 1

Time 2 Factors

2 3 4

(a) TRA Items


028 027 018 017 002 008

-026

005 013 003

-011 009

-009 000

089 088 080 009 006 017

-012



EOU 1 -008 084 008 EOU2 00 1 090 003 EOU3 -005 091 003 EOU4 010 091 007

Eigen 483 335 151 Var 323 223 101 Cum 323 546 647






TABLE 4


Time 1 Time 2



U EOU

D ACC

05 1

-


EOU D

ACC

050

-

U = EOU 002

Final Models


Ul EOU






6 Conclusions





























Management Scl 27 ( 1981) 1309-1323










































































References







httpwwwjstororg












httpwwwjstororg

























5 Empirical Study










Qziesf ionnaire




Results





TABLE l






Usage (Time 2) = BI 012 040 BI 035 063

(2) TRA BI = A + SN

A SN

(3) TAM B l = A + U

A U

A = U + EOU U

EOU

U = EOU EOU






TABLE 2


T ~ m e1 Time 2



A SN

C hip

(b) BI = A + U + SN A

u SN


U 058 035 E 001 018

C hie 010 032h










TABLE 3


Belief Item 1 2

Time I Factors

3 4 5 1

Time 2 Factors

2 3 4

(a) TRA Items


028 027 018 017 002 008

-026

005 013 003

-011 009

-009 000

089 088 080 009 006 017

-012



EOU 1 -008 084 008 EOU2 00 1 090 003 EOU3 -005 091 003 EOU4 010 091 007

Eigen 483 335 151 Var 323 223 101 Cum 323 546 647






TABLE 4


Time 1 Time 2



U EOU

D ACC

05 1

-


EOU D

ACC

050

-

U = EOU 002

Final Models


Ul EOU






6 Conclusions





























Management Scl 27 ( 1981) 1309-1323










































































References







httpwwwjstororg












httpwwwjstororg










Qziesf ionnaire




Results





TABLE l






Usage (Time 2) = BI 012 040 BI 035 063

(2) TRA BI = A + SN

A SN

(3) TAM B l = A + U

A U

A = U + EOU U

EOU

U = EOU EOU






TABLE 2


T ~ m e1 Time 2



A SN

C hip

(b) BI = A + U + SN A

u SN


U 058 035 E 001 018

C hie 010 032h










TABLE 3


Belief Item 1 2

Time I Factors

3 4 5 1

Time 2 Factors

2 3 4

(a) TRA Items


028 027 018 017 002 008

-026

005 013 003

-011 009

-009 000

089 088 080 009 006 017

-012



EOU 1 -008 084 008 EOU2 00 1 090 003 EOU3 -005 091 003 EOU4 010 091 007

Eigen 483 335 151 Var 323 223 101 Cum 323 546 647






TABLE 4


Time 1 Time 2



U EOU

D ACC

05 1

-


EOU D

ACC

050

-

U = EOU 002

Final Models


Ul EOU






6 Conclusions





























Management Scl 27 ( 1981) 1309-1323










































































References







httpwwwjstororg












httpwwwjstororg





TABLE l






Usage (Time 2) = BI 012 040 BI 035 063

(2) TRA BI = A + SN

A SN

(3) TAM B l = A + U

A U

A = U + EOU U

EOU

U = EOU EOU






TABLE 2


T ~ m e1 Time 2



A SN

C hip

(b) BI = A + U + SN A

u SN


U 058 035 E 001 018

C hie 010 032h










TABLE 3


Belief Item 1 2

Time I Factors

3 4 5 1

Time 2 Factors

2 3 4

(a) TRA Items


028 027 018 017 002 008

-026

005 013 003

-011 009

-009 000

089 088 080 009 006 017

-012



EOU 1 -008 084 008 EOU2 00 1 090 003 EOU3 -005 091 003 EOU4 010 091 007

Eigen 483 335 151 Var 323 223 101 Cum 323 546 647






TABLE 4


Time 1 Time 2



U EOU

D ACC

05 1

-


EOU D

ACC

050

-

U = EOU 002

Final Models


Ul EOU






6 Conclusions





























Management Scl 27 ( 1981) 1309-1323










































































References







httpwwwjstororg












httpwwwjstororg






TABLE 2


T ~ m e1 Time 2



A SN

C hip

(b) BI = A + U + SN A

u SN


U 058 035 E 001 018

C hie 010 032h










TABLE 3


Belief Item 1 2

Time I Factors

3 4 5 1

Time 2 Factors

2 3 4

(a) TRA Items


028 027 018 017 002 008

-026

005 013 003

-011 009

-009 000

089 088 080 009 006 017

-012



EOU 1 -008 084 008 EOU2 00 1 090 003 EOU3 -005 091 003 EOU4 010 091 007

Eigen 483 335 151 Var 323 223 101 Cum 323 546 647






TABLE 4


Time 1 Time 2



U EOU

D ACC

05 1

-


EOU D

ACC

050

-

U = EOU 002

Final Models


Ul EOU






6 Conclusions





























Management Scl 27 ( 1981) 1309-1323










































































References







httpwwwjstororg












httpwwwjstororg










TABLE 3


Belief Item 1 2

Time I Factors

3 4 5 1

Time 2 Factors

2 3 4

(a) TRA Items


028 027 018 017 002 008

-026

005 013 003

-011 009

-009 000

089 088 080 009 006 017

-012



EOU 1 -008 084 008 EOU2 00 1 090 003 EOU3 -005 091 003 EOU4 010 091 007

Eigen 483 335 151 Var 323 223 101 Cum 323 546 647






TABLE 4


Time 1 Time 2



U EOU

D ACC

05 1

-


EOU D

ACC

050

-

U = EOU 002

Final Models


Ul EOU






6 Conclusions





























Management Scl 27 ( 1981) 1309-1323










































































References







httpwwwjstororg












httpwwwjstororg



TABLE 4


Time 1 Time 2



U EOU

D ACC

05 1

-


EOU D

ACC

050

-

U = EOU 002

Final Models


Ul EOU






6 Conclusions





























Management Scl 27 ( 1981) 1309-1323










































































References







httpwwwjstororg












httpwwwjstororg





























Management Scl 27 ( 1981) 1309-1323










































































References







httpwwwjstororg












httpwwwjstororg











































































References







httpwwwjstororg












httpwwwjstororg












httpwwwjstororg


00. kel_1 davis,bagozzi and warshaw_1989_user acceptance of computer technology _a comparison of two...

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peoples intentions

available computer systems

enduser systems

perceived ease of use

computerbased tools

perceived usefulness

peoples computer ac

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